Skill UI

A comprehensive reference architecture guide for implementing enterprise-grade integrations with Customer.io. It details best practices for building robust, scalable, and reliable systems, focusing on event-driven patterns, message queuing (e.g., Redis/Kafka), service layer separation, and idempotency. Ideal for designing complex data pipelines and ensuring high availability in mission-critical applications.

This guide provides technical patterns for building fault-tolerant integrations with Customer.io. Learn to implement critical resilience techniques, including Circuit Breakers to prevent cascading failures, Retry with Jitter for handling transient errors, and Fallback Queues (using Redis) to ensure data continuity during service outages. Essential for robust, production-grade applications.

This guide details production-ready patterns for integrating the Customer.io SDK (customerio-node). It covers best practices including building type-safe clients, implementing robust retry logic with exponential backoff for network stability, performing efficient event batching for high-volume data streams, and managing singleton lifecycles. Use these patterns to build scalable, reliable, and maintainable user behavior tracking systems.

A comprehensive guide for safely upgrading and migrating the Customer.io Node SDK. It details the necessary steps for handling major version changes, such as transitioning from the legacy CustomerIO class to the modern TrackClient and APIClient pattern. Use this skill when refactoring old code, adapting to breaking API changes, or ensuring compatibility with the latest SDK best practices.

Automates D2 diagram creation guidance for visual content requests, offering best practice patterns, ready-to-use code/config snippets, and validation against standards whenever users mention the tool or need chart/diagram support.

Defines how to automate Dadata Ru operations through Composio’s Rube MCP, covering prerequisite connection checks, schema-aware tool discovery, execution patterns, and common pitfalls so agents can run reliable workflows with updated tool interfaces.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Automates guidance for data catalog updater tasks, covering ETL, transformation, orchestration, and streaming workflows to keep pipeline metadata accurate.

Guides data engineering patterns for ETL pipelines, Spark analytics, and quality checks, showcasing Python and SQL snippets to extract, transform, load, validate, and model sales data in modern warehouses.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output validation.